Weapons Of Math Destruction

NEW YORK TIMES BESTSELLER • A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric—with a new afterword “A manual for the twenty-first-century citizen . . . relevant and urgent.”—Financial Times NATIONAL BOOK AWARD LONGLIST • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review • The Boston Globe • Wired • Fortune • Kirkus Reviews • The Guardian • Nature • On Point We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules. But as mathematician and data scientist Cathy O’Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination—propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data.

Buy now to get the main key ideas from Cathy O'Neil's Weapons of Math Destruction Technological advances are often lauded as unbiased and fair. But in Weapons of Math Destruction (2016), data scientist Cathy O’Neil posits that the opposite is true. Today’s world is largely ruled by mathematical algorithms that decide most aspects of life, from education to work, insurance to elections. Existing models have proven to be harmful. They promote inequalities by favoring the wealthy and the privileged and pushing minorities and poor people further down. These models, by being invisible, unregulated, and indisputable, form Weapons of Math Destruction fueled by the Big Data that could divide the world even more if left unchecked.

The summary of Weapons of Math Destruction – How Big Data Increases Inequality and Threatens Democracy presented here include a short review of the book at the start followed by quick overview of main points and a list of important take-aways at the end of the summary. The Summary of The 2016 film Weapons of Math Destruction takes a critical look at the increasing number of algorithms that may be affecting your day-to-day life in ways that you are not even aware of. An increasing number of people are being negatively impacted as a result of the increased use of algorithms to automate work processes in an increasing number of organisations and services, including schools and the police. Find out what you can do to protect yourself and your data so that you aren't at the mercy of automation and don't put yourself in that position. Weapons of Math Destruction summary includes the key points and important takeaways from the book Weapons of Math Destruction by Cathy O’Neil. Disclaimer: 1. This summary is meant to preview and not to substitute the original book. 2. We recommend, for in-depth study purchase the excellent original book. 3. In this summary key points are rewritten and recreated and no part/text is directly taken or copied from original book. 4. If original author/publisher wants us to remove this summary, please contact us at [email protected].

* Our summary is short, simple and pragmatic. It allows you to have the essential ideas of a big book in less than 30 minutes. By reading this summary, you will discover that mathematical models, and more particularly algorithms coupled with information systems, may increase inequalities and threaten democracies. You will also discover that : mathematical models are not neutral, but hide ideologies and personal interests; algorithms promise efficiency and lowest cost, but increase inequalities and injustices; mathematical formulas affect your life choices; your personal data are weapons used by the giants of Tech. At a time when algorithms are king, the decisions that affect your life - which school to go to, which loan to take out - are no longer made by humans, but by mathematical models. In theory, this should promote fairness: everyone is judged by the same level of value. Mathematician Cathy O'Neil argues the opposite. These opaque, unregulated models can cause irreparable damage, like the mortgage payments of American households during the subprime crisis in 2007. Worse: they accentuate discrimination. For example, a student from a modest background who cannot obtain a loan - too risky - will never have access to quality education. These mathematical models support the lucky ones and disadvantage the oppressed: welcome to the dark side of big data, the exponential growth of digital data! *Buy now the summary of this book for the modest price of a cup of coffee!

Please note: This is a companion version & not the original book. Sample Book Insights: #1 The Indians’ manager, Lou Boudreau, used statistical analysis to turn Ted Williams’s hits into outs. Today, every player is treated like Ted Williams, and teams analyze every variable they can quantify and attach a value to. #2 The term Moneyball is now used to describe any statistical approach in domains long ruled by the gut. Baseball is a good example of a transparent model, as everyone can access the stats and understand how they’re interpreted. #3 The model I use to manage my family’s food is a simplified version of the real world’s complexity. It is always making mistakes, because it is, by its very nature, a simplification. #4 Models are created to simplify a complex world into a simple version that can be easily understood and from which important facts and actions can be inferred. They are opinions embedded in mathematics.

Cathy O'Neil is a quant, and her experience comes across in her entertaining book “Weapons of Math Destruction.” It is an insider's disillusioned tale about the dark side of large scale predictive analytics. These data and models affect many areas of our life from the financial markets where we invest our savings, to selecting educational institutions, the labor market, our consumer behavior such as obtaining insurance and credit, our civic life, and even the prison system. The author cautions us against the religious fervor with which we, as a society, are embracing quantitative models in our quest for efficiency. She shows how models are biased, often built on flimsy understanding, lack a self-correcting feedback loop and accountability, and tend to create their own reality at the expense of the poor and the vulnerable. The author's demands for more transparency in algorithms and the sacrificing of mathematical efficiency in the interest of fairness as a society should be a catalyst for important policy conversations. The title of the book might be an homage to Warren Buffet's 2002 Berkshire annual report where citing flawed models and counterparty concentration risk (among others) he famously called derivatives, financial weapons of mass destruction.